A Bearingless Permanent Magnet Synchronous Motor Controller Based on Flux Observer

Abstract

The invention discloses a bearingless permanent magnet synchronous motor controller based on a flux observer, which is used for direct torque and direct suspension force control of a bearingless permanent magnet synchronous motor. Through a torque winding flux observer, a direct torque controller and a torque winding voltage source inverter, the torque of the bearingless permanent magnet synchronous motor is directly controlled; through a suspension force winding flux observer, a direct suspension force controller, a suspension force estimation model and a suspension force winding voltage source inverter, a suspension force of the bearingless permanent magnet synchronous motor is directly controlled; and first-order delays of part of input signals are used as inputs of a neural network, and novel flux observers of a torque winding and a suspension force winding are designed by adopting the improved BP neural network, so that the flux size, the phase and the torque of the torque windingand the flux size and the phase of the suspension winding can be accurately estimated, and the stability of a control system is improved.

Description

technical field [0001] The invention belongs to the technical field of electric drive control equipment, in particular to a controller for controlling a bearingless permanent magnet synchronous motor, which is used for direct torque and direct suspension force control of the bearingless permanent magnet synchronous motor. Background technique [0002] Bearingless permanent magnet synchronous motor is a new type of special motor with high speed, high precision and no need for lubrication. It has more and more application prospects in aerospace, chemical manufacturing, semiconductor industry and other fields that require special environments. The direct control of bearingless permanent magnet synchronous motor has the advantages of fast dynamic response and good robustness. It includes direct torque control and direct suspension force control, and has a wide range of applications in many fields. Direct control requires flux calculation, and the accuracy of traditional flux cal...

AI Technical Summary

Problems solved by technology

The direct measurement method is greatly affected by noise interference and motor parameter errors; the voltage model method is implemented in a purely integral way, which will generate cumulative errors and cause the integration result to shift; the new flux linkage integration method is based on a first-order low-pass filter. Basically, the output flux linkage is brought back for feedback, and an algorithm between pure integral and low-pass filter link is established. The control effect is remarkable, but the introduction of the low-pass filter changes the amplitude and phase of the flux linkage. , it is more serious at low speed; the least squares support vector machine has strong nonlinear expression ability and strong generalization ability, but the disadvantage lies in the complex structure and large amount of calculation, which puts forward higher requirements for digital processing chips in practical applications. It takes a lot of time; the artificial neural network uses its excellent fitting ability to approach the flux observation system of the motor, and has a strong learning ability, but the commonly used static neural network lacks the necessary feedback and dynamic structure, which affects the system dynamics. performance

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